@article {418,
	title = {Statistical processing of large image sequences},
	journal = {IEEE Transactions on Image Processing},
	volume = {14},
	year = {2005},
	pages = {80 - 93},
	abstract = {<p>The dynamic estimation of large-scale stochastic image sequences, as frequently encountered in remote sensing, is important in a variety of scientific applications. However, the size of such images makes conventional dynamic estimation methods, for example, the Kalman and related filters, impractical. We present an approach that emulates the Kalman filter, but with considerably reduced computational and storage requirements. Our approach is illustrated in the context of a 512 times; 512 image sequence of ocean surface temperature. The static estimation step, the primary contribution here, uses a mixture of stationary models to accurately mimic the effect of a nonstationary prior, simplifying both computational complexity and modeling. Our approach provides an efficient, stable, positive-definite model which is consistent with the given correlation structure. Thus, the methods of this paper may find application in modeling and single-frame estimation.</p>
},
	keywords = {Algorithms, Artificial Intelligence, Automated, Biological, Cluster Analysis, computational complexity, Computer Graphics, Computer Simulation, Computer-Assisted, correlation methods, correlation structure, dynamic estimation method, image enhancement, image interpretation, image sequences, Imaging, Information Storage and Retrieval, Kalman filter, Kalman filters, large-scale stochastic image sequence estimation, Models, Movement, Numerical Analysis, ocean surface temperature, Pattern Recognition, Remote Sensing, Reproducibility of Results, Sensitivity and Specificity, Signal Processing, single-frame estimation, static estimation step, Statistical, statistical analysis, statistical image processing, stochastic processes, Subtraction Technique, Three-Dimensional, Video Recording},
	issn = {1057-7149},
	doi = {http://dx.doi.org/10.1109/TIP.2004.838703},
	author = {F M. Khellah and P Fieguth and M J. Murray and M R. Allen}
}
@inproceedings {533,
	title = {Modeling the correlation structure of images in the wavelet domain},
	booktitle = {14th Canadian Conference on Electrical and Computer Engineering},
	year = {2001},
	address = {Toronto},
	abstract = {<p>In this paper we investigate the correlation structure of the wavelet coefficients corresponding to random fields. The context of this work is the study of Bayesian approaches to wavelet shrinkage for the purposes of image denoising. This paper concentrates on both within-scale and across-scale statistical dependencies for a variety of wavelets and random fields, with examples provided for both 1-D and 2-D signals. The results show the whitening effect of the wavelet transform to be quite clear-even for particular highly correlated spatial processes the within-scale correlation decays exponentially fast, however the correlation between scales is surprisingly substantial, even for separations several scales apart. Our goal, initiated in this paper, is the development of an efficient random field model, describing these statistical correlations, and the demonstration of its effectiveness in the context of Bayesian wavelet shrinkage for signal and image denoising</p>
},
	keywords = {1-D signals, 2-D signals, across-scale statistical dependencies, Bayes methods, Bayesian approaches, correlation structure, correlation theory, Image Denoising, image enhancement, random field model, random fields, wavelet coefficients, wavelet domain, wavelet shrinkage, wavelet transforms, white noise, whitening effect, within-scale statistical dependencies},
	doi = {http://dx.doi.org/10.1109/CCECE.2001.933599},
	author = {Z Azimifar and P Fieguth and E Jernigan}
}
